一种新的求解广义多跳洗牌网络平均跳距的计算模型

本文深入研究了用于波分复用光波通信的广义多跳洗牌网络的平均跳距性能。作者使用的广义洗牌网络概念最早见于洗牌环网络。基于上述定义的洗牌网络可以分为两类：附加级网络和缩减级网络。本文引用了计算附加级网络平均跳距的精确模型,并分析和推导了一种新的计算缩减级网络平均跳距的近似模型。     

无线传感器网络中平均跳距修正的DV-Hop定位算法

针对基本无线传感器网络DV-Hop定位算法中,计算平均跳距时产生偏小误差的原因,提出了基于平均跳距修正的DV-Hop算法。改进算法中,引入信标节点个数比例、所有信标节点平均跳距的最大值以及所有信标节点平均跳距的平均值三个因子,并采用它们对平均跳距进行修正。通过Matlab软件对算法进行仿真验证,结果表明,改进后的DV-Hop算法有效降低了未知节点的定位误差,是一种可行的无线传感器网络节点定位解决方案。     

跳数和跳距修正的距离向量跳段定位改进算法

由于在无线传感网络中传感节点随机分布,致使距离向量跳段(DV-Hop)定位算法的定位误差偏大,为此,采用跳数和跳距修正的方法对距离向量跳段定位算法进行改进。在计算信标节点和未知节点跳数的过程中引入节点通信距离的影响,使得节点之间的实际跳数计算更加准确;再利用线性搜索算法获取最优信标节点间的平均跳距,使信标节点的平均跳距更加精确。对比仿真实验结果表明,改进算法大大提升了定位的精度,提升幅度高达15%。     

基于平均跳距修正的三维DV-Hop定位算法研究

本文在基于三维DV-Hop定位算法的基础上,提出了一种基于平均跳距修正的三维DV-Hop定位算法.该算法除了将DV-Hop定位算法从二维空间扩展到三维空间以外,还对未知节点到锚节点的平均每跳距离作了相应的修正,仿真结果表明:与原始算法相比改进后的算法定位精度有了一定提高.     

无线传感器节点定位技术的研究及改进

无线传感器网络节点数量大且均随机分布的特点,导致了传统DV-HOP算法中计算距离所用的锚节点平均跳距及未知节点校正值存在较大误差。针对以上问题,本文提出了锚节点平均跳距的修正及校正值计算的改进算法,提高了定位精度,减小了计算量,增强了定位实时性。     

局部网络中的TOKEN—PASSING技术

本文通过对ＴＯＫＥＮ－ＰＡＳＳＩＮＧ协议与ＣＳＭＡ协议的比较，说明ＴＯＫＥＮ－ＰＡＳＳＩＮＧ技术克服了ＣＳＭＡ协议的不足，从实例中阐述了该技术在局部网络中的重要地位及其优点。     

短波宽带快速跳频通信技术研究

介绍了一种新型的短波高速跳频通信系统－－ＣＨＥＳＳ系统,描述了ＣＨＥＳＳ系统的结构并对其性能进行了分析。重点讨论了ＣＨＥＳＳ系统的核心技术－－差分跳频技术（ＤＦＨ）,提出了一种相关跳频编码方案。用计算机模拟实现了ＣＨＥＳＳ系统的调制解调、编码解码过程,由此验证了ＣＨＥＳＳ系统的可行性。     

一种基于加权处理的无线传感器网络平均跳距离估计算法

定位技术是无线传感器网络的关键技术之一,传统DV-Hop定位算法只考虑了最近一个锚节点估计的平均跳距离值,而单个锚节点估计的平均跳距离值无法准确地反映网络的实际平均跳距离。本文提出了一种基于加权处理的平均跳距离估计算法,考虑多个锚节点估计的平均跳距离值,根据距离未知节点的跳数进行加权,使网络平均跳距离的估计更加准确,从而提高定位精度。仿真结果表明,与DV-Hop算法的平均跳距离估计算法相比,本文算法更准确地估计平均跳距离,降低了均方根误差,并提高了定位精度。     

改进人工免疫算法优化的DV-Hop节点定位算法

由于无线传感器网络连通性不合理,导致计算待测节点与已知节点间距离时存在误差。为此,提出一种改进的人工免疫算法(AIA)优化DV-Hop未知节点坐标。首先对原平均跳距加权,其次利用网络中信标节点间距离产生的偏差构造跳距校正值得到最终的全网平均跳距。最后在计算待测节点坐标时引入AIA,针对AIA易陷入局部最优以及收敛速度过慢的问题,在局部搜索过程中采用高斯变异方法对AIA进行改进,扩大搜索范围,得到优化的待测节点坐标。经Matlab仿真证明,与原DV-Hop算法相比,改进后的算法在节点总数、信标节点比例以及通信半径三方面平均定位误差降低了近15%左右,具有较高的定位精度和较好的定位稳定性,同时也改善了算法的收敛性。     

慢跳频的伪随机序列

本文基于ＧＦ（２）域上的增广质数序列,提出了一类ＧＦ（Ｐ＾Ｋ）域上的增广质数跳频序列,对其差的游程特性进行了改进,并根据慢跳频系统的要求进行了宽间隔处理。计算机模拟分析表明：宽间隔增广质数跳频序列具有良好的汉明相关特性,是一类优良的跳频序列,适合于慢跳频系统。     

Generalized Survivable Network

Two important requirements for future backbone networks are full survivability against link failures and dynamic bandwidth provisioning. We demonstrate how these two requirements can be met by introducing a new survivable network concept called the generalized survivable network (GSN), which has the special property that it remains survivable no matter how traffic is provisioned dynamically, as long as the input and output constraints at the nodes are fixed. A rigorous mathematical framework for designing the GSN is presented. In particular, we focus on the GSN capacity planning problem, which finds the edge capacities for a given physical network topology with the input/output constraints at the nodes. We employ fixed single-path routing which leads to wide-sense nonblocking GSNs. We show how the initial, infeasible formal mixed integer linear programming formulation can be transformed into a more feasible problem using the duality transformation. A procedure for finding the realizable lower bound for the cost is also presented. A two-phase approach is proposed for solving the GSNCPP. We have carried out numerical computations for ten networks with different topologies and found that the cost of a GSN is only a fraction (from 39% to 97%) more than the average cost of a static survivable network. The framework is applicable to survivable network planning for ASTN/ASON, VPN, and IP networks as well as bandwidth-on-demand resource allocation.     

Congestion management techniques for disruption‐tolerant satellite networks

Delay and disruption‐tolerant networks are becoming an appealing solution for extending Internet boundaries toward challenged environments where end‐to‐end connectivity cannot be guaranteed. In particular, satellite networks can take advantage of a priori trajectory estimations of nodes to make efficient routing decisions. Despite this knowledge is already used in routing schemes such as contact graph routing, it might derive in congestion problems because of capacity overbooking of forthcoming connections (contacts). In this work, we initially extend contact graph routing to provide enhanced congestion mitigation capabilities by taking advantage of the local traffic information available at each node. However, since satellite networks data generation is generally managed by a mission operation center, a global view of the traffic can also be exploited to further improve the latter scheme. As a result, we present a novel strategy to avoid congestion in predictable delay‐ and disruption‐tolerant network systems by means of individual contact plans. Finally, we evaluate and compare the performance improvement of these mechanisms in a typical low Earth orbit satellite constellation.     

基于图正则化非负矩阵分解的二分网络社区发现算法

现实世界存在大量二分网络,研究其社区结构有助于从新角度认识和理解异质复杂网络。非负矩阵分解模型能够克服二分结构的限制,有效地挖掘二分网络的潜在结构,但也存在着时间复杂度高、收敛慢等问题。该文提出一种基于图正则化的三重非负矩阵分解(NMTF)算法应用于二分网络社区发现,通过图正则化将用户子空间和目标子空间的内部连接关系作为约束项引入到三重非负矩阵分解模型中;同时将NMTF分解为两个最小化近似误差的子问题,并给出了乘性迭代算法以交替更新因子矩阵,从而简化矩阵分解迭代,加快收敛速度。实验和分析证明：对于计算机生成网络和真实网络,该文提出的社区划分方法均表现出较高的准确率和稳定性,能够快速准确地挖掘二分网络的社区结构。     

基于图论的无线传感器网络并行禁忌搜索信道分配方法

Wireless sensor networks are suffering from serious frequency interference. In this paper, we propose a channel assignment algorithm based on graph theory in wireless sensor networks. We first model the conflict infection graph for channel assignment with the goal of global optimization minimizing the total interferences in wireless sensor networks. The channel assignment problem is equivalent to the generalized graph coloring problem which is a NP complete problem. We further present a meta heuristic Wireless Sensor Network Parallel Tabu Search (WSN PTS) algorithm, which can optimize global networks with small numbers of iterations. The results from a simulation experiment reveal that the novel algorithm can effectively solve the channel assignment problem.     

具有不完全可靠节点的无向网络终端对可靠性评价方法

本文分析了NPR/T算法处理无向网络时产生错误的原因,提出了一种适用于具有不完全可靠节点无向网络的终端对可靠性评价方法.该方法通过生成特定有向图结构的事件树,消除了处理无向网络时带来的错误.在算法没有运行完成的情况下,仍可得到终端对可靠度的上下界.理论分析和实验结果表明,本文算法性能优于ENR/KW等其它算法.     

An exploration of aggregation convergecast scheduling

We consider the problem of aggregation convergecast scheduling as it applies to wireless networks. The solution to aggregation convergecast satisfies the aggregation process, expressed as precedence constraints, combined with the impact of the shared wireless medium, expressed as resource constraints. Both sets of constraints influence the routing and scheduling. We propose an aggregation tree construction suitable for aggregation convergecast that is a synthesis of a tree tailored to precedence constraints and another tree tailored to resource constraints. Additionally, we show that the scheduling component can be modeled as a mixed graph coloring problem. Specifically, the extended conflict graph is introduced, and through it, a mapping from aggregation convergecast to mixed graphs is described. In the mixed graph, arcs represent the precedence constraints and edges represent the resource constraints. The mixed graph chromatic number corresponds to the optimal schedule length. Bounds for the graph coloring are provided and a branch-and-bound strategy is subsequently developed from which we derive numerical results that allow a comparison against the current state-of-the-art heuristic.     

有源网络完全k树多项式的产生及其在网络分析中的应用

本文算法产生有源网络无源树边的完全k树多项式,算法时间复杂度与列写无向图全部树的改进的Minty算法相同。用该算法分析有源网络的符号函数可有效地减少对消冗余项数,同时也避免了对无源完全树边的符号鉴别问题。文章讨论了算法的合理性,并举例说明了它在网络分析中的应用。     

Minimum latency joint scheduling and routing in wireless sensor networks

Wireless sensor networks are expected to be used in a wide range of applications from environment monitoring to event detection. The key challenge is to provide energy efficient communication; however, latency remains an important concern for many applications that require fast response. In this paper, we address the important problem of minimizing average communication latency for the active flows while providing energy-efficiency in wireless sensor networks. As the flows in some wireless sensor network can be long-lived and predictable, it is possible to design schedules for sensor nodes so that nodes can wake up only when it is necessary and asleep during other times. Clearly, the routing layer decision is closely coupled to the wakeup/sleep schedule of the sensor nodes. We formulate a joint scheduling and routing problem with the objective of finding the schedules and routes for current active flows with minimum average latency. By constructing a novel delay graph, the problem can be solved optimally by employing the M node-disjoint paths algorithm under FDMA channel model. We further present extensions of the algorithm to handle dynamic traffic changes and topology changes in wireless sensor networks.     

可靠通信网多总线结构的超图设计法

本文在处理机数和可靠度给定情况下，先构造出成本最小、连通性最好图即核度最小图；将此图作为一类超图的代表图、救是其中阶数最小及端口数最小的超图的对偶图；最后根据对偶图，作出其对应的成本最小，可靠性最高的多总线结构。     

Graph‐based optimal asynchronous scheduling in large propagation delay underwater acoustic sensor networks

Optimal scheduling is essential to minimize the time wastage and maximize throughput in high propagation delay networks such as in underwater and satellite communication. Understanding the drawbacks of synchronous scheduling, this paper addresses an asynchronous optimal scheduling problem to minimize the time wastage during the transmission. The proposed scheduling problem is analyzed in both broadcast and non‐broadcast networks, which is highly applicable in high propagation delay networks. In broadcast networks, the proposed scheduling method reduces to a graph‐theoretic model that is shown to be equivalent to the classic algorithmic asymmetric traveling salesman problem (TSP) which is NP‐Hard. Although it is NP‐Hard, the TSP is well‐investigated with many available methods to find the best solution for up to tens of thousands of nodes. In non‐broadcast networks, the optimal solution to the scheduling problem considers the possibility of parallel transmission, which is optimized using graph coloring algorithm. The groups obtained through graph coloring are solved using Asymmetric Traveling Salesman algorithm to obtain the optimal schedule. The proposed method efficiently solves the scheduling problem for networks of practical size.